Phonemic Resonance Networks: A Bio-Acoustic Framework for Emotion-Aware Speech Synthesis

Posted: Nov 24, 2024

• Dr. Elara Vance
• Prof. Kaito Tanaka
• Dr. Sofia Petrova

Abstract

This paper introduces Phonemic Resonance Networks (PRNs), a novel computational framework that bridges bio-acoustic principles with neural architectures to create emotionally intelligent speech synthesis systems. Unlike conventional text-to-speech approaches that treat emotion as a superficial modulation layer, PRNs fundamentally reconceptualize speech generation through the lens of vocal tract biomechanics and acoustic resonance patterns observed in human emotional expression. Our methodology draws inspiration from the physiological changes in human vocal production during emotional states, modeling how specific emotions alter formant structures, glottal pulse characteristics, and articulatory precision. The core innovation lies in our resonance embedding layer, which transforms phonemic representations into multi-dimensional resonance spaces that capture the subtle acoustic signatures of emotional states. We developed a hybrid architecture combining convolutional neural networks for spectral pattern recognition with recurrent components for temporal dynamics, all governed by bio-physical constraints derived from vocal fold vibration models and articulatory phonetics. Experimental validation involved a novel dataset of 1,200 speech samples across eight emotional states, collected with simultaneous electroglottographic measurements to capture ground-truth vocal fold behavior. Results demonstrate that PRNs achieve 47% higher emotional naturalness ratings compared to state-of-the-art emotional TTS systems, while maintaining 99.2% intelligibility. More significantly, our analysis reveals that the learned resonance embeddings form interpretable clusters corresponding to physiological emotion families, providing unprecedented insight into the acoustic correlates of emotional speech. This work establishes a new paradigm for emotion-aware speech synthesis that is grounded in the biomechanical reality of human vocal production, opening avenues for more authentic human-computer interaction and therapeutic applications in speech pathology.